ReviewPolicy, politics & nursing practice2015
Challenges Associated With Using Large Data Sets for Quality Assessment and Research in Clinical Settings.
Review in Policy, politics & nursing practice, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Digital Health Data Quality Issues: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Intravitreal Anti-VEGF Injection Transitions Across Bevacizumab, Biosimilar, and Branded Medications: The IRIS Registry Study.Journal of vitreoretinal diseases · 2026Article
- Quality and patient safety surveillance systems in healthcare settings: a rapid realist review.BMJ open · 2026Review
- Artificial Intelligence in the Diagnosis and Management of Pulmonary Tuberculosis: A Review of Current Applications and Future Perspectives.Therapeutics and clinical risk management · 2026Review
- Data Duplication and Errors in Large Medical Data Sets: A Case Study in the IRIS® Registry.Ophthalmology science · 2026Article
- Insights into motor impairment assessment using myographic signals with artificial intelligence: a scoping review.Biomedical engineering letters · 2025Review
- An Approach to "Big Data".Plastic surgery (Oakville, Ont.) · 2024Article
- Review
- Clinical Data Warehousing: A Scoping Review.Journal of the Society for Clinical Data Management · 2024Article
- Clinical Annotation and Segmentation Tool (CAST) Implementation for Dental Diagnostics.Cureus · 2023Article
- PRIME-IPD SERIES Part 1. The PRIME-IPD tool promoted verification and standardization of study datasets retrieved for IPD meta-analysis.Journal of clinical epidemiology · 2021Article
- Data extraction for epidemiological research (DExtER): a novel tool for automated clinical epidemiology studies.European journal of epidemiology · 2021Article
- Utility of the Current Procedural Terminology Codes for Prophylactic Stabilization for Defining Metastatic Femur Disease.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2020Article
- Article
- Strategies for overcoming language barriers in research.Journal of advanced nursing · 2020Article
- Applying STOPP Guidelines in Primary Care Through Electronic Medical Record Decision Support: Randomized Control Trial Highlighting the Importance of Data Quality.JMIR medical informatics · 2017Article
- Incidence and physiological mechanism of carboplatin-induced electrolyte abnormality among patients with non-small cell lung cancer.Oncotarget · 2017Article
- The Need for a Definition of Big Data for Nursing Science: A Case Study of Disaster Preparedness.International journal of environmental research and public health · 2016Review
- Comparison of Measures to Predict Mortality and Length of Stay in Hospitalized Patients.Nursing researchArticle
- Electronic Surveillance of Surgical Site Infections.Surgical infectionsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
The rapidly expanding use of electronic records in health-care settings is generating unprecedented quantities of data available for clinical, epidemiological, and cost-effectiveness research. Several challenges are associated with using these data for clinical research, including issues surrounding access and information security, poor data quality, inconsistency of data within and across institutions, and a paucity of staff with expertise to manage and manipulate large clinical data sets. In this article, we describe our experience with assembling a data-mart and conducting clinical research using electronic data from four facilities within a single hospital network in New York City. We culled data from several electronic sources, including the institution's admission-discharge-transfer system, cost accounting system, electronic health record, clinical data warehouse, and departmental records. The final data-mart contained information for more than 760,000 discharges occurring from 2006 through 2012. Using categories identified by the National Institutes of Health Big Data to Knowledge initiative as a framework, we outlined challenges encountered during the development and use of a domain-specific data-mart and recommend approaches to overcome these challenges.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.